1. Field of the Invention
The present invention relates to an image processing apparatus and method capable of suppressing noise in a digital image with edges maintained intact. The present invention further relates to a program for causing a computer to execute the method.
2. Description of the Related Art
Recently, digital still cameras (digital camera) have been spreading rapidly. The digital cameras focus an optical image on a digital device (such as a CCD, photoelectron multiplier tube, or the like) instead of on a silver halide film, and the image may be recorded on a recording medium as digital data (digital image) or used directly by a computer. Thus, they are useful for recording digital images after performing image processing thereon, or the like.
Further, it is often the case in which images obtained by the conventional method of focusing them on a silver halide film, or those obtained by printing on a printing medium, such as a photo paper, paper, or the like, are read out by a readout device such as a scanner or the like. Then, the digital data so obtained are recorded or subjected to image processing like the images obtained by digital cameras.
Such digital images are reproduced as images displayed on a display unit, printed on a photo paper, or the like. The reproduced images are expected to have a high image quality so that various image processing techniques need to be performed on the digital images.
Of the image processing techniques, noise suppression for a digital image has a greater impact on the quality of the processed image, so that various techniques are proposed. Generally, a low pass filter (LPF) is used for suppressing the noise. The LPF, however, has a drawback that it degrades edges in the image signals and the entire image is blurred, although it suppresses the noise in the image. Therefore, it is desirable to balance between the noise suppression and prevention of edge degradation by figuring out the noise level of the image and regulating the noise suppression level.
Conventionally, the criteria for noise level assessment include RMS granularity that uses the standard deviation of density, or Wiener spectrum obtained by Fourier transforming the density variation. The noise suppression methods using these criteria include, for example, a method in which a chart without density variation is imaged by a digital camera, and RMS granularity or Wiener spectrum is calculated to obtain the noise characteristic of the digital camera. Thereafter, when performing noise suppression on an image obtained by the digital camera, the noise suppression is performed by predicting the noise level of the image based on the noise characteristic of the digital camera obtained in advance.
Another noise suppression method is also proposed. In the method, a flat region is extracted from an objective image itself to obtain the noise level, which is used as the noise level of the image.
In the mean time, the luminance component of an image constituting the edge components of the image greatly contributes to the human vision. Therefore, when performing noise suppression on an image, it is desirable to perform the noise suppression by obtaining the noise level of the luminance component. Japanese Unexamined Patent Publication No. 10(1998)-003539 discloses a noise suppression method, in which the directional differential is taken for each pixel in an image to extract the edge component at the pixel position, and determination is made if it is an edge or noise based on the edge ratio to regulate the noise suppression level.
The methods for suppressing the noise includes, for example, a method that makes use of the fact that most of the noise components present in a high frequency component of an image as small amplitude signals, and uses a two dimensional ε-filter to separate and remove the small amplitude high frequency noise. As described earlier, the LPF commonly used for noise suppression may suppress the noise component, but at the same time degrade edges in the signals, thereby entire image is blurred. In contrast, the ε-filter has a property to smooth only the variation of a small amplitude signal waveform, so that the application of the ε-filter to an image allows the edges involving steep level variations to be maintained, and the sharpness of the entire image remains substantially intact. Basically, the ε-filter behaves such that a nonlinear transformation function is applied to the level variation of amplitude in the high frequency component of an image, and the value obtained thereby is subtracted from the original image signal. The nonlinear transformation function is a function that makes the output zero if the signal amplitude is greater than a predetermined threshold value. That is, if the ε-filter is applied, the output of the nonlinear transformation function is zero in the area of an image where the amplitude is greater than the predetermined threshold value, and the original signal is maintained in the processed image, while in the area where the amplitude is smaller than the predetermined threshold value, the signal value thereof becomes the value obtained by subtracting the output value of the nonlinear transformation function (absolute value is greater than zero) from the original signal value. This allows the area having noise (area where the amplitude is smaller than the threshold value) to be smoothed, and the higher amplitude edge area (area where the amplitude is greater than the threshold value) to be maintained. The threshold value used in the method is the parameter for distinguishing between edge and noise areas.
As described above, the use of the ε-filter, for example, may satisfy both the noise suppression and edge maintainability. The performance of the ε-filter, however, is greatly influenced by the threshold value set for distinguishing between noise and edge areas. An inappropriately set threshold value may cause adverse effects, such as insufficient noise suppression, or blurred image (dull edge) due to erroneous suppression of the edge area as noise. Therefore, how to set an appropriate threshold value is a challenging problem. Further, the use of a filter, such as E-filter, median filter or the like which is expected to have the identical effect to that of the ε-filter for noise suppression, how to set an appropriate threshold value is still a challenging problem.
In view of the circumstances described above, it is an object of the present invention to provide an image processing method and apparatus having high noise suppression and edge maintenance capabilities achieved by determining the amount of noise in image data, setting an appropriated threshold value used for distinguishing between the noise and edge areas according to the noise amount determination result, and performing noise suppression using a filter with the threshold value set on the filter. It is a further object of the present invention to provide a program for causing the image processing apparatus to execute the image processing method of the present invention.